This repository is primarily maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), artificial intelligence security, vulnerability research, exploit development, reverse engineering, and more.

🐢 Open-Source Evaluation & Testing library for LLM Agents

企业级 AI 编程助手，专为 研发协作 和 研发管理 场景而设计。

ToolHive makes deploying MCP servers easy, secure and fun

A curated list of useful resources that cover Offensive AI.

A curated list of AI-powered coding tools

A list of backdoor learning resources

a security scanner for custom LLM applications

AI Captcha Bypass

Reconmap is a collaboration-first security operations platform for infosec teams and MSSPs, enabling end‑to‑end engagement management, from reconnaissance through execution and reporting. With built-in command automation, output parsing, and AI‑assisted summaries, it delivers faster, more structured, and high‑quality security assessments.

A security scanner for your LLM agentic workflows

MCP Monitoring with eBPF

MCP for Security: A collection of Model Context Protocol servers for popular security tools like SQLMap, FFUF, NMAP, Masscan and more. Integrate security testing and penetration testing into AI workflows.

All-in-one offensive security toolbox with AI agent and MCP architecture. Integrates tools like Nmap, Metasploit, FFUF, SQLMap. Enables pentesting, bug bounty hunting, threat hunting, and reporting. RAG-based responses with local knowledge base support.

A deliberately vulnerable banking application designed for practicing Security Testing of Web App, APIs, AI integrated App and secure code reviews. Features common vulnerabilities found in real-world applications, making it an ideal platform for security professionals, developers, and enthusiasts to learn pentesting and secure coding practices.

RuLES: a benchmark for evaluating rule-following in language models

Toolkits to create a human-in-the-loop approval layer to monitor and guide AI agents workflow in real-time.

Project CodeGuard is an AI model-agnostic security framework and ruleset that embeds secure-by-default practices into AI coding workflows (generation and review). It ships core security rules, translators for popular coding agents, and validators to test rule compliance.

Framework for testing vulnerabilities of large language models (LLM).

A curated list of academic events on AI Security & Privacy